Outranking-Based Trace Clustering
Overview
Implements outranking-based trace clustering for process mining. Pairwise similarity between traces is assessed on multiple, problem-specific criteria using ELECTRE-III-style partial concordance and discordance indices with indifference, similarity and veto thresholds. The aggregated credibility matrix is then clustered using normalized spectral clustering or hierarchical clustering. Also includes outlier trimming, must-link/cannot-link adjustments, validation indices and diagnostic plots.
- **Breaking:** the quantile-threshold constructor
q()is renamed toas_quantile()to avoid maskingbase::q()and for consistency withas_traces(). Replaceq(0.8)withas_quantile(0.8). - Added the maintainer's ORCID.
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-04
- NOTE12 OK, 1 NOTE2026-08-01
- OK6 OK2026-07-20
Documentation
- Help pages
- 16
- Examples that run
- 100%14 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 89%
- Return-value docs
- 100%
- Help pages citing references
- 13%
Downloads
Repository
Commits over time
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Dependencies
Installing simOutrank adds 2 packages (2 with compiled code, 1.3 MB of source tarballs). Heaviest: stringdist 843 KB, lpSolve 457 KB.
Code & Tests
Datasets
People & History
- 0.3.0Latest2026-07-19 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-07-19
- Total releases
- 1 / 1 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.5
- Bundled data
- 0.5 KB / 1 file
- Download size
- 143 KB
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("simOutrank")The package provides a citation file that builds its entry when R runs it. Run citation() to see it.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-22, which the citation names so these numbers can be found later. More on citing and the projects behind them.